Resumo:
The growing need to ensure the efficiency and reliability of the National Interconnected
System (SIN), which represents the backbone of the country’s energy
distribution, is what drives this work. Faced with the challenges posed by the
territorial vastness and complexity of the Brazilian electrical grid, the use of Phasor
Measurement Units (PMUs) emerges as a promising solution for real-time network
monitoring. However, the effectiveness of this monitoring is linked to the ability to
detect and respond to anomalies quickly and accurately, minimizing the risks of failures
and supply interruptions. The study addresses the challenge of managing and
evaluating the country’s interconnected electrical grid, where accurate information
is pertinent for preventive and corrective actions in a continental-scale distribution
system. The core of this research lies in the innovative exploration of advanced data
compression techniques combined with unsupervised machine learning algorithms,
aiming to optimize the interpretation and analysis of the large volumes of data
generated by the PMUs. This approach points to a significant improvement in the
quality and precision of the information extracted and offers a scalable solution to
the challenge of processing and analyzing the data from a large-scale distribution
system. The effectiveness is assessed through the detection of significant events and
anomalies in the network at points geographically distant from the event’s origin.
The results of this study validate the efficacy of the proposed algorithms, highlighting
their practical relevance and substantial impact on improving the quality
and reliability standards in the electricity supply. They demonstrate the feasibility
of implementing these techniques in real scenarios, showcasing the potential for
transformation in failure prevention and critical event management, contributing
to a more stable, effective, and secure energy distribution network.
